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Horizontal vacuum belt filters are used for continuous solid–liquid separation in a wide variety of industrial processes. Despite the low pressure difference (usually Δp < 0.8 bar), the high air pumping requirement to maintain the pressure difference results in considerable energy consumption. In this article, the specific energy consumption of vacuum filtration and air flow rates of a pilot-scale horizontal vacuum belt filter unit are investigated. The results show that a claw-type vacuum pump consumes only half the energy compared to a conventional liquid ring vacuum pump at corresponding operating points. A comparison between the specific energy consumption of vacuum filtration and thermal drying of the filter cake to zero moisture revealed that vacuum filtration accounted for less than half of the total energy consumption in the applied experimental conditions at Δp = 0.2–0.5 bar. The majority of the total pumping requirement of the pilot-scale filter resulted from leaks, and only 2–25% of the air flow found its way through the cake and the filter medium. The results suggest that there is a combination of the pressure difference level and the mass of solids deposited per filtration area that together with thermal drying consumes the least amount of energy per solids mass. [ABSTRACT FROM AUTHOR]

• A novel soft sensor method has been developed and evaluated. • Evaporation of void space water cools vacuum filter cake and suction air. • Change in suction air temperature correlates with filter cake solids contents. • Temperature change information increases the accuracy of moisture content estimates. • Conditions for minimum specific energy consumption were identified. The moisture content of filter cakes is probably the most important characteristic that should be kept at a desired level in industrial cake filtration applications to maintain consistent product quality and minimize energy consumption. Most of the currently applied methods for contactless real-time monitoring of the moisture content are based for example on x-ray or microwave techniques, and therefore, the equipment for the purpose is highly specialized. This paper introduces a novel soft sensor for filter cake moisture estimation that uses machine learning algorithms and data collected with basic process instrumentation. The method is primarily based on the cooling effect observed in the cake and air, caused by evaporation of liquid from the cake during the dewatering period, and it can be supported by other process data. The specific energy consumption of vacuum filtration and the subsequent thermal drying to zero moisture is also analyzed. The results of pilot-scale experiments with calcite slurry and a horizontal belt vacuum filter show that in order to minimize the specific energy consumption of vacuum filtration, it is crucial to find the right combination of slurry concentration, vacuum level, and mass of filter cake per unit area. The proposed method for estimating the filter cake moisture content is especially suitable for real-time monitoring and control, enabling also considerable reduction in the energy consumption of the overall process. When applying the proposed soft sensor method in a pilot-scale process, the mean absolute error of the estimated moisture content of the filter cake is ∼0.4 percentage points when the temperature of air at the vacuum pump inlet and the vacuum pump air flow rate are included in the input variables. [ABSTRACT FROM AUTHOR]